Uncertainty in magnetic activity indices

Xu, Weihua

doi:10.1007/s11431-008-0261-z

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…It is found that there are still some differences in reflecting the onset and the level of the disturbance. Xu [2008] discussed three categories of uncertainties in magnetic indices: “data uncertainty” originating from inadequate data processing, “station uncertainty” caused by incomplete station covering, and “physical uncertainty” stemming from unclear physical mechanism. A conventional approach to calculate Sq , computing the mean of the H component in the five quietest days of each month, is used in the r H index calculation.…”

Section: Discussionmentioning

confidence: 99%

A sensitive geomagnetic activity index for space weather operation

Zhao

et al. 2010

Space Weather

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1] There is an ongoing demand for real-time geomagnetic indices in space services. The traditional 3 h K index and K-derived planetary indices cannot issue alters promptly during large storms, and the 3 h interval is much larger than the time scales of ionospheric responses. To overcome these difficulties, we define a new consecutive and linear geomagnetic activity index, the range of hourly H component index (r H ) with 1 min resolution, and develop a local r H index nowcast system for space weather operation, which can issue geomagnetic storm alerts quickly. We also derive Kp/Ap indices conveniently from a single station data to describe the global geomagnetic activity. Then we make a statistic comparison between r H and other definite index values during storm and find that r H is sensitive to the geomagnetic disturbance and can reflect the geomagnetic activity more delicately.

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Section: Discussionmentioning

confidence: 99%

A sensitive geomagnetic activity index for space weather operation

Zhao

et al. 2010

Space Weather

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“…Inputs to solar and geomagnetic parameterizations usually are measured directly and are physically meaningful, but nowcasting and shortterm forecasting of parameters like the Kp remain a topic of continuing research [44,45]. Challenges include limited station observability coverage and limited representation of associated physical mechanisms [46].…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias

Durazo

Kostelich

Mahalov

2021

Front. Appl. Math. Stat.

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The dynamics of many models of physical systems depend on the choices of key parameters. This paper describes the results of some observing system simulation experiments using a first-principles model of the Earth’s ionosphere, the Thermosphere Ionosphere Electrodynamics Global Circulation Model (TIEGCM), which is driven by parameters that describe solar activity, geomagnetic conditions, and the state of the thermosphere. Of particular interest is the response of the ionosphere (and predictions of space weather generally) during geomagnetic storms. Errors in the overall specification of driving parameters for the TIEGCM (and similar dynamical models) may be especially large during geomagnetic storms, because they represent significant perturbations away from more typical interactions of the earth-sun system. Such errors can induce systematic biases in model predictions of the ionospheric state and pose difficulties for data assimilation methods, which attempt to infer the model state vector from a collection of sparse and/or noisy measurements. Typical data assimilation schemes assume that the model produces an unbiased estimate of the truth. This paper tests one potential approach to handle the case where there is some systematic bias in the model outputs. Our focus is on the TIEGCM when it is driven with solar and magnetospheric inputs that are systematically misspecified. We report results from observing system experiments in which synthetic electron density vertical profiles are generated at locations representative of the operational FormoSat-3/COSMIC satellite observing platforms during a moderate (G2, Kp = 6) geomagnetic storm event on September 26–27, 2011. The synthetic data are assimilated into the TIEGCM using the Local Ensemble Transform Kalman Filter with a state-augmentation approach to estimate a small set of bias-correction factors. Two representative processes for the time evolution of the bias in the TIEGCM are tested: one in which the bias is constant and another in which the bias has an exponential growth and decay phase in response to strong geomagnetic forcing. We show that even simple approximations of the TIEGCM bias can reduce root-mean-square errors in 1-h forecasts of total electron content (a key ionospheric variable) by 20–45%, compared to no bias correction. These results suggest that our approach is computationally efficient and can be further refined to improve short-term predictions (∼1-h) of ionospheric dynamics during geomagnetic storms.

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